HIV-associated immunosuppression portends high risk for cryptococcal meningitis (CM). Despite progress that has increased HIV testing and anti-retroviral therapy (ART) availability in resource-limited settings, the incidence and mortality of CM remain very high. In addition, ART can elicit immune reconstitution inflammatory syndrome (C-IRIS), and CM also occurs in HIV- persons, including recipients of solid organ transplants and biologics. There are no host biomarkers to predict which HIV-infected (HIV+) patients will develop CM or C- IRIS. We are interested in the role antibody (Ab) immunity may play in resistance to CM, which is rare in people with intact immunity. We have contributed significant knowledge in this area, including recent data showing lower natural Laminarin (a ?-glucan)-binding Ab levels in HIV+ patients with C-IRIS and positive serum cryptococcal antigen tests. Studies of HIV+ and HIV- patients revealed perturbations in Ab repertoires of patients with CM and C-IRIS, and lower IgM memory B cell levels in patients with CM. In mice, IgM memory homologs, B-1 cells and/or nave IgM enhanced resistance to CM. These cells make Abs that bind microbial carbohydrates, e.g. the ?-glucans on the Cn cell wall. The goal of this project is to probe the link between natural Ab and B cell responses to Cn and resistance to CM. We hypothesize CM risk will associate with deficiency of specific Cn- and/or ?-glucan-binding Abs, stemming from B cell repertoire pertubations. Our aims are 1] To characterize Cn- and ?-glucan-binding Abs of patients with and at risk for CM and C-IRIS, isolate monoclonal antibodies (huMabs) from normal persons and test their efficacy in mouse models. 2] To determine the ability of huMabs and GXM- and ?-glucan-binding Abs to 1] exert direct effects on Cn viability and biology, and/or 2] mediate human effector cell Cn phagocytosis and/or killing. 3] To analyze B cell subsets, VH/VL repertoires, and transcriptional pathways of patients with and at risk for CM and C-IRIS, and controls, and use statistical modeling to identify signatures of CM risk. Knowledge gained from this project will inform tools to predict CM and C-IRIS risk, overcome roadblocks to diagnosis and therapy, provide new insight into the pathogenesis of CM, and inform development of immunotherapy and vaccines.